Sylvie Lelandais, Jean Triboulet, Malik Mallem, Camille Edie Nzi, Djamel Merad, Triboulet, Jean, Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2), INPHB (INPHB), Institut National Polytechnique de Grenoble (INPG), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Informatique, Biologie Intégrative et Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Systèmes Complexes (LSC), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Our problem is a diagnostic task. Due to environment degraded conditions, direct measurements are not possible. Due to the rapidity of the machine, human intervention is not possible in case of position fault. So, an oriented vision solution is proposed. The problem must be solved for high velocity industrial tooling machines. Degraded conditions: vibrations, water and chip of metal projections, dazzling..., are present all the time. Image analysis in constraint environment depends on constraint importance. Before tooling, the vision system has to answer: “is it the right piece at the right place?” Complementary methods presented in this paper are proposed in an adaptive way to solve this diagnostic problem. This detection is made by comparing an acquired camera image of the piece to be tooled with an image of reference. Some image processing methods are performed and combined in order to extract 2D features. Some of these 2D features are evaluated and used as parameters in a diagnostic process. After a data analysis, image parameters are reduced. First, we present an overview of image processing methods generally used to solve that kind of problem and their limitations in our particular degraded case. In order to obtain automatic and robust classification, two methods are implemented. The first one is based on Bayes technique that provides a good classification in case of fault presence. The second method is based on neural networks and provides good results in case of images without faults. These two methods give a global rate of good classification greater than 90%, for 720 images acquired from an industrial site. Keywords: diagnostic task, 2D vision, video camera, image matching, bayes classifier, neural classifier Global Journal of Pure and Applied Sciences Vol. 12(2) 2006: 229-238